Global warming: the risk of being a sceptic

It appears after all that global warming is man-made. However, its main cause is not the accumulation of carbon oxide but the heat generated by the intense controversies over the scientific reality of global warming and the viability of statistical and mathematical models developed to explain its causes and forecast its future evolution. This statement may be excessive but so is the tenor of the debate. The topic has always been highly contentious and was object of spirited debate among various academics and researchers. However, in the last few months, the intensity rose noticeably as the result of increased the political and media visibility of the issue. Catastrophic events such as Asian tsunami or Catherina hurricane were perceived as tangible signs of global warming. For their part, governments sympathetic to the hypothesis of man-made global warming sponsored exhaustive, data-laden studies, which sought make the case airtight and overwhelm the opposition. Thus, there was the Stern study, published last August and proclaimed by Tony Blair, whose government commissioned the study, to be the most important piece of policy work, ever carried out by the UK government. Most recently and more spectacularly, a French government, under a direct patronage of Jacques Chirac, organised in early February in Paris, a “Citizens of the Earth” conference for Global Ecological Governance, bringing together several hundred scientists and senior officials from national government and international organisations. During that meeting, the Fourth report of the French Intergovernmental Panel on Climate Change (IPPC, or GIEC in French) was published, which sought to demonstrate through exhaustive empirical studies and sophisticated forecasting models that man-made global warming is incontrovertible.

The result of all this is the emergence of conventional wisdom (“pensée unique” in French) about the global warming that makes a reasoned discussion and contradictory debate quite difficult, even among supposedly open-minded intellectuals. I realised the strength of the conventional wisdom last Tuesday in Paris during a meeting of group of economists, brought together by the CEO of leading retail services company, Laser. For the most part, members of group, which meets once a month, are eminent academics, sceptical about the merits of neo-classical theory but respectful of the tradition of Political Economy, which includes Adam Smith, Karl Marx and John Maynard Keynes. Our topic of discussion this time was the climate change, in particular Stern report. Many participants thought this report marked a radical break with past practices and a recognition by an eminent member of the global elite that business as usual not is no longer working for global warming. As I have written few months a blog on the report, I circulated it to the members and made two points (a) Stern recommendations do not represent a radical break with the past, his main point being that a speedy action could be relatively painless in terms of economic growth and therefore acceptable to policy makers (b) as much as I respect the work of Stern In the ensuing discussion, I sensed a sense of unease about what was perceived as a minimisation of global warming both a threat to the survival of humanity and as a policy lever, which would create an economic upheaval on a scale of industrial or information revolution. One participant called my position provocative; another praised the quality of GIEC work, and still another wondered whether I appreciated the magnitude of political groundswell (as evidenced by popular success of refuse recycling). No voices were raised and the discussion remained very polite, if animated but I felt among my colleagues a degree of surprise that a well-informed person would not share the conventional wisdom. And the conclusion of the meeting was that we should propose “something,” preferably international and incentive- rather tax-based, to tackle global warming, particularly in the emerging superpowers, India and China. I am very curious about such proposals and looking forward to our next meeting when they will be presented.

For all my frustration about being misunderstood by my colleagues, I should consider myself lucky. According to the UK media, it would appear that in the UK those who dare to express doubts are threatened with bodily harm, even death.

Yet, risk of ostracism notwithstanding, I do remain sceptical particularly about the weather forecasting models. I spent considerable amount of time looking at financial forecasting models, whether for the macroeconomic or financial parameters (GDP growth and inflation, interest and exchange rates and market trends). Those models attracted the best brains of thousands of researchers and powerful computers, using highly sophisticated methods. And yet, their track record is far from stellar or consistent and their forecasting time horizon is usually confined to few months.

Yet, climate is an infinitely more complex phenomenon (or a set of phenomena) than financial markets. The idea that a model based on current scientific methods and data can reliably forecast weather evolution fifty years with any degree of chronological and geographical is far-fetched, to say the least. If GIEC scientists really believe in their models, I suggest that they spend some time with financial “quants” in investment banks. This will make them either more modest and realistic, or, conversely, very rich, thus generating sizeable resources to combat global warning.

From long tail to variegated tails

For close to 200 years, social scientists lived under the tyranny of normal distribution, which focused their attention on most probable outcomes and on the averages, means and medians. We now need to be careful not to commit the same error with the long-tail distribution. Book on this subject, The Long Tail, became a bestseller and a required reading for management consultants and investment analysts. Its author, Chris Anderson, has been promoting the long-tail persistently and intelligently, using all traditional and new media channels at his disposal as editor of Wired. We are firm believers in the pertinence of long-tail. There is however a risk of overselling and overstating the importance of long-tail in the economy and society.

Under a long-term distribution, probability of various events is widely dispersed rather than concentrated: low frequency events represent a larger share of the total than under normal distribution. Thus, in order to understand the future and earn money in e-commerce, one no longer needs to focus exclusively on most probable events or on biggest selling items. The main message of long tail is the increasing variety. This message also applies to statistical distributions itself. Normal distribution will remain dominant, as long as random events dominate all aspects of economic and social interactions. Furthermore, there is more than one family of long-tail distributions. Those discussed by Anderson follow the power law, according to which relative frequency of events is inversely proportional to their size. One example of power law is the frequency of earthquakes: bigger earthquakes happen less frequently than the big ones. The relationship between the two is regular and scale independent. The only parameter that changes is speed at which the frequency decreases. Anderson argues that as the costs of search on the Internet are drastically cut, frequency decreases more slowly. However, while this may be true for purchase of books and records, it is not necessarily true for the ranking of online bookselling and music shops (or search engines for that matter). Such ranking usually follows Zipf or Pareto power law, which reflects oligopolistic concentration, where the biggest firm is twice as big as the second largest, which three times bigger than the third largest, etc. Those laws are also called 20/80, as 20% of firms generate 80% of revenues.

An important characteristic of Anderson distribution is that there is no significant variation in the value of events. If this assumption is relaxed and large variation is allowed, a new type of distribution appears - the so called Gauchy distribution. Under Gauchy, events at the end of tail can take extreme values. Thus Gauchy allows the modelling of low-frequency, high impact events. Airline accidents provide a good example here. These are considerably, very considerably, less frequent than car-driving accidents (official US statistics indicate about one accident for 600 000 flying hours.) Yet, should the accident happen, the chances of survival are much much lower for airline accident than for a car crash. The implication of this fact is that air safety cannot be approached using normal distribution and looking at most probable events (which is no accident). The notion of average has no operational meaning. Airline operators need to focus on extreme cases and plan their safety measures for extremely low probabilities.

Cauchy distribution is significant in financial markets. Fro instance, Cauchy distribution arises when one seeks to compare listed UK and German software companies. If one ignores value, UK software sector appears better performing than the German one: more listed companies in a greater variety of sectors and application. But one cannot ignore the value: the leading German company is SAP and its market cap is close to 50 billion euros, ten times larger than the best-capitalised UK company (Sage plc), and over thirty times larger than the second biggest German company (Software AG).

Thus, while long-tail distribution directs our attention to weak signals, Cauchy distribution demonstrates the non-linearity of low probability events. Both of them (and a normal distribution as well) are necessary to understand the interactions of chance and purposefulness in our complex world.